Fu et al. [1]}, and Wang et al. [2]} have been among the first ones to exploit cnns tackling the counting task with the density estimation and regression techniques. In particular, the authors of [1]} proposed a cnn for crowd density estimation; they significantly improved the estimation speed by removing some network connections according to the observation of the existence of similar feature maps.
On the other hand, the authors in [2]} proposed an end-to-end deep cnn model able to estimate the number of people present in images of highly dense crowds, regressing from the image features to the count. They exploited the AlexNet network [5]}, where the final fully-connected layers of 4096 neurons, initially responsible for classifying objects, have been replaced by a single neuron layer in charge of predicting the count. Besides, training data is augmented with additional negative samples whose ground truth count is set to zero to reduce false predictions like buildings and trees.
